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ResNet Model Automatically Extracts and Identifies FT-NIR Features for Geographical Traceability of...

ResNet Model Automatically Extracts and Identifies FT-NIR Features for Geographical Traceability of...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_79fc201d9a27469db9ed3a1143aa9e19

ResNet Model Automatically Extracts and Identifies FT-NIR Features for Geographical Traceability of Polygonatum kingianum

About this item

Full title

ResNet Model Automatically Extracts and Identifies FT-NIR Features for Geographical Traceability of Polygonatum kingianum

Publisher

Switzerland: MDPI AG

Journal title

Foods, 2022-11, Vol.11 (22), p.3568

Language

English

Formats

Publication information

Publisher

Switzerland: MDPI AG

More information

Scope and Contents

Contents

Medicinal plants have incredibly high economic value, and a practical evaluation of their quality is the key to promoting industry development. The deep learning model based on residual convolutional neural network (ResNet) has the advantage of automatic extraction and the recognition of Fourier transform near-infrared spectroscopy (FT-NIR) feature...

Alternative Titles

Full title

ResNet Model Automatically Extracts and Identifies FT-NIR Features for Geographical Traceability of Polygonatum kingianum

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_doaj_primary_oai_doaj_org_article_79fc201d9a27469db9ed3a1143aa9e19

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_79fc201d9a27469db9ed3a1143aa9e19

Other Identifiers

ISSN

2304-8158

E-ISSN

2304-8158

DOI

10.3390/foods11223568

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